Postdoctoral Research Associate - Multiscale Composite Modeling

Updated: 2 months ago
Location: Oak Ridge, TENNESSEE

Requisition Id 12243 

Overview: 

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

 

We are seeking a Postdoctoral Research Associate who will support the Carbon and Composites Group in the Chemical Sciences Division, Physical Sciences Directorate at Oak Ridge National Laboratory (ORNL).  

 

Major Duties/Responsibilities:

  • First-principles modeling to the design of multifunctional composites.
  • Establish multiscale correlations between materials chemistry, thermomechanical properties and functional properties including simulating their responsive behavior to applied fields.
  • Conduct density functional theory (DFT) calculations to formulate machine learning interatomic potentials tailored for multifunctional composites.
  • Formulate multiscale coupling schemes, integrating from first principle calculations to molecular dynamics and continuum-level finite element analysis while incorporating uncertainty quantification.
  • Application of classical or machine learning-based methods (e.g., surrogate modeling) for the inverse molecular formulation of multifunctional polymers/nanocomposites.
  • Contribute to the strategic planning, execution, and assessment of research initiatives, which involves delineating project tasks, devising timelines, tracking task advancement, and compiling and scrutinizing activity reports.
  • Disseminate research findings via various channels, including reports, technology demonstrations, conference presentations, and scientific paper publications, as appropriate and deemed beneficial.
  • Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.

 

Basic Qualifications:

•     A Ph.D. in computational science and engineering, structural, mechanical, or aerospace engineering, polymer science and engineering, or materials science and engineering, completed within the last 5 years.

 

Preferred Qualifications:

•     Expertise in computational materials physics and solid mechanics.

•     Demonstrate experience in formulating and implementing models in widely used materials modeling, including but not limited to density functional theory and molecular dynamics

•     Possess excellent programming skills.

•     Have practical experience with machine learning models.

•     Additional proficiency in level-set algorithm

•     Exhibit exceptional written and oral communication skills

•     Show high motivation and a commitment to safety.

•     Highly motivated and demonstrated ability to work independently with autonomy while contributing creatively to collaborative environments.

•     Utilize effective multitasking skills within time constraints, independently prioritizing and completing various tasks to meet deadlines.

 

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.

 

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to [email protected] with the position title and number referenced in the subject line.

 

Instructions to upload documents to your candidate profile:

  • Login to your account via jobs.ornl.gov
  • View Profile
  • Under the My Documents section, select Add a Document

 

Benefits at ORNL:  

ORNL offers competitive pay and benefits programs to attract and retain talented people. The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience.

 

Other benefits include: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Life Insurance, Pet Insurance, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

 

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: [email protected] .

 

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.


If you have trouble applying for a position, please email [email protected].


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.


Nearest Major Market: Knoxville



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